Overview

Dataset statistics

Number of variables39
Number of observations18857
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.9 MiB
Average record size in memory270.0 B

Variable types

Numeric23
Categorical10
Boolean6

Warnings

OPPORTUNITY_ID has a high cardinality: 18857 distinct values High cardinality
ACCOUNT_ID has a high cardinality: 14468 distinct values High cardinality
SUB_REGION_C has a high cardinality: 76 distinct values High cardinality
OWNER_SUB_REGION_C has a high cardinality: 144 distinct values High cardinality
DNA_STD_DC_LEAD_SOURCE has a high cardinality: 53 distinct values High cardinality
DNA_CUSTOM_DC_PREV_OPPS_COUNT is highly correlated with DNA_CUSTOM_DC_PREV_OPPS_LOST_COUNT and 1 other fieldsHigh correlation
DNA_CUSTOM_DC_PREV_OPPS_LOST_COUNT is highly correlated with DNA_CUSTOM_DC_PREV_OPPS_COUNT and 1 other fieldsHigh correlation
DNA_STD_DC_MKTG_NURTURE_TIME is highly correlated with DNA_CUSTOM_DC_PREV_OPPS_COUNT and 1 other fieldsHigh correlation
DNA_CUSTOM_DC_CONTACTS_ACTIVE is highly correlated with DNA_STD_DC_MKTG_TOTAL_IA_COUNTHigh correlation
DNA_STD_DC_EVENTS_IA_AFTER_OPPTY_COUNT is highly correlated with DNA_STD_DC_EVENTS_TOTAL_IA_COUNTHigh correlation
DNA_STD_DC_EVENTS_TOTAL_IA_COUNT is highly correlated with DNA_STD_DC_EVENTS_IA_AFTER_OPPTY_COUNTHigh correlation
DNA_STD_DC_MKTG_TOTAL_IA_COUNT is highly correlated with DNA_CUSTOM_DC_CONTACTS_ACTIVEHigh correlation
DNA_STD_DC_END_RESULT is highly correlated with DAYS_IN_NEGOTIATE_CLOSE_C and 1 other fieldsHigh correlation
DNA_CUSTOM_DC_PREV_OPPS_COUNT is highly correlated with DNA_CUSTOM_DC_PREV_OPPS_LOST_COUNT and 3 other fieldsHigh correlation
DNA_CUSTOM_DC_PREV_OPPS_LOST_COUNT is highly correlated with DNA_CUSTOM_DC_PREV_OPPS_COUNT and 1 other fieldsHigh correlation
DAYS_IN_BUSINESS_JUSTIFICATION_C is highly correlated with DAYS_IN_NEGOTIATE_CLOSE_C and 1 other fieldsHigh correlation
DAYS_IN_NEGOTIATE_CLOSE_C is highly correlated with DNA_STD_DC_END_RESULT and 2 other fieldsHigh correlation
DAYS_IN_PO_WITH_CHANNEL_C is highly correlated with DNA_STD_DC_END_RESULT and 2 other fieldsHigh correlation
DNA_STD_DC_MKTG_NURTURE_TIME is highly correlated with DNA_CUSTOM_DC_PREV_OPPS_COUNT and 4 other fieldsHigh correlation
DNA_CUSTOM_DC_CONTACTS_ACTIVE is highly correlated with DNA_STD_DC_MKTG_TOTAL_IA_COUNTHigh correlation
DNA_STD_DC_EVENTS_IA_AFTER_OPPTY_COUNT is highly correlated with DNA_STD_DC_EVENTS_TOTAL_IA_COUNTHigh correlation
DNA_STD_DC_EVENTS_IA_BEFORE_OPPTY_COUNT is highly correlated with DNA_STD_DC_EVENTS_TOTAL_IA_COUNTHigh correlation
DNA_STD_DC_EVENTS_TOTAL_IA_COUNT is highly correlated with DNA_STD_DC_MKTG_NURTURE_TIME and 4 other fieldsHigh correlation
DNA_STD_DC_MKTG_IA_AFTER_OPPTY_COUNT is highly correlated with DNA_STD_DC_MKTG_TOTAL_IA_COUNTHigh correlation
DNA_STD_DC_MKTG_TOTAL_IA_COUNT is highly correlated with DNA_CUSTOM_DC_PREV_OPPS_COUNT and 5 other fieldsHigh correlation
DNA_STD_DC_TASKS_IA_AFTER_OPPTY_COUNT is highly correlated with DNA_CUSTOM_DC_PREV_OPPS_COUNT and 3 other fieldsHigh correlation
DNA_STD_DC_END_RESULT is highly correlated with DAYS_IN_NEGOTIATE_CLOSE_C and 1 other fieldsHigh correlation
DNA_CUSTOM_DC_PREV_OPPS_COUNT is highly correlated with DNA_CUSTOM_DC_PREV_OPPS_LOST_COUNT and 2 other fieldsHigh correlation
DNA_CUSTOM_DC_PREV_OPPS_LOST_COUNT is highly correlated with DNA_CUSTOM_DC_PREV_OPPS_COUNT and 1 other fieldsHigh correlation
DAYS_IN_BUSINESS_JUSTIFICATION_C is highly correlated with DAYS_IN_NEGOTIATE_CLOSE_C and 1 other fieldsHigh correlation
DAYS_IN_NEGOTIATE_CLOSE_C is highly correlated with DNA_STD_DC_END_RESULT and 2 other fieldsHigh correlation
DAYS_IN_PO_WITH_CHANNEL_C is highly correlated with DNA_STD_DC_END_RESULT and 2 other fieldsHigh correlation
DNA_STD_DC_MKTG_NURTURE_TIME is highly correlated with DNA_CUSTOM_DC_PREV_OPPS_COUNT and 4 other fieldsHigh correlation
DNA_STD_DC_EVENTS_IA_AFTER_OPPTY_COUNT is highly correlated with DNA_STD_DC_EVENTS_TOTAL_IA_COUNTHigh correlation
DNA_STD_DC_EVENTS_IA_BEFORE_OPPTY_COUNT is highly correlated with DNA_STD_DC_EVENTS_TOTAL_IA_COUNTHigh correlation
DNA_STD_DC_EVENTS_TOTAL_IA_COUNT is highly correlated with DNA_STD_DC_MKTG_NURTURE_TIME and 4 other fieldsHigh correlation
DNA_STD_DC_MKTG_IA_AFTER_OPPTY_COUNT is highly correlated with DNA_STD_DC_MKTG_TOTAL_IA_COUNTHigh correlation
DNA_STD_DC_MKTG_TOTAL_IA_COUNT is highly correlated with DNA_CUSTOM_DC_PREV_OPPS_COUNT and 4 other fieldsHigh correlation
DNA_STD_DC_TASKS_IA_AFTER_OPPTY_COUNT is highly correlated with DNA_STD_DC_MKTG_NURTURE_TIME and 2 other fieldsHigh correlation
DNA_STD_AC_NUMBER_OF_EMPLOYEES is highly correlated with SUB_REGION_CHigh correlation
DNA_CUSTOM_DC_INCUMBENT is highly correlated with SUB_REGION_C and 1 other fieldsHigh correlation
DNA_CUSTOM_DC_PREV_OPPS_COUNT is highly correlated with DNA_STD_DC_MKTG_IA_AFTER_OPPTY_COUNT and 2 other fieldsHigh correlation
ADVANCED_PAST_STAGE_2_C is highly correlated with ADVANCED_PAST_STAGE_1_C and 1 other fieldsHigh correlation
DNA_CUSTOM_DC_CONTACTS_ACTIVE is highly correlated with DNA_STD_DC_MKTG_TOTAL_IA_COUNT and 1 other fieldsHigh correlation
DNA_STD_DC_EVENTS_IA_AFTER_OPPTY_COUNT is highly correlated with DNA_STD_DC_EVENTS_IA_BEFORE_OPPTY_COUNT and 1 other fieldsHigh correlation
DNA_STD_DC_MKTG_IA_AFTER_OPPTY_COUNT is highly correlated with DNA_CUSTOM_DC_PREV_OPPS_COUNT and 4 other fieldsHigh correlation
DNA_STD_DC_MKTG_IA_BEFORE_OPPTY_COUNT is highly correlated with DNA_CUSTOM_DC_PREV_OPPS_COUNT and 4 other fieldsHigh correlation
DNA_STD_DC_LEAD_SOURCE is highly correlated with SUB_REGION_C and 1 other fieldsHigh correlation
CONVERTED_FROM_LEAD_C is highly correlated with df_indexHigh correlation
DNA_STD_DC_EVENTS_IA_BEFORE_OPPTY_COUNT is highly correlated with DNA_STD_DC_EVENTS_IA_AFTER_OPPTY_COUNT and 1 other fieldsHigh correlation
df_index is highly correlated with CONVERTED_FROM_LEAD_CHigh correlation
DNA_STD_DC_EVENTS_TOTAL_IA_COUNT is highly correlated with DNA_STD_DC_EVENTS_IA_AFTER_OPPTY_COUNT and 4 other fieldsHigh correlation
DAYS_IN_DISCOVERY_C is highly correlated with DNA_ML_OPPORTUNITY_LIFE_DAYSHigh correlation
DAYS_IN_TECHNICAL_VALIDATION_C is highly correlated with DNA_ML_OPPORTUNITY_LIFE_DAYSHigh correlation
OPPORTUNITY_SUB_TYPE_C is highly correlated with SUB_REGION_CHigh correlation
SUB_REGION_C is highly correlated with DNA_STD_AC_NUMBER_OF_EMPLOYEES and 5 other fieldsHigh correlation
DNA_CUSTOM_DC_SEGMENT is highly correlated with SUB_REGION_CHigh correlation
DNA_STD_DC_LEAD_SOURCE_INBOUND is highly correlated with DNA_STD_DC_LEAD_SOURCEHigh correlation
DNA_CUSTOM_DC_PREV_OPPS_LOST_COUNT is highly correlated with DNA_CUSTOM_DC_PREV_OPPS_COUNT and 3 other fieldsHigh correlation
DAYS_IN_CONSENSUS_C is highly correlated with DNA_ML_OPPORTUNITY_LIFE_DAYSHigh correlation
DNA_STD_DC_MKTG_TOTAL_IA_COUNT is highly correlated with DNA_CUSTOM_DC_CONTACTS_ACTIVE and 6 other fieldsHigh correlation
DNA_STD_DC_TASKS_IA_AFTER_OPPTY_COUNT is highly correlated with DNA_CUSTOM_DC_CONTACTS_ACTIVE and 1 other fieldsHigh correlation
DNA_ML_OPPORTUNITY_LIFE_DAYS is highly correlated with DAYS_IN_DISCOVERY_C and 2 other fieldsHigh correlation
ADVANCED_PAST_STAGE_1_C is highly correlated with ADVANCED_PAST_STAGE_2_CHigh correlation
DNA_CUSTOM_DC_PRIMARY_COMPETITOR is highly correlated with DNA_CUSTOM_DC_INCUMBENTHigh correlation
DNA_STD_DC_END_RESULT is highly correlated with ADVANCED_PAST_STAGE_2_CHigh correlation
DNA_STD_DC_LEAD_SOURCE_INBOUND is highly correlated with DNA_STD_DC_LEAD_SOURCEHigh correlation
DNA_STD_DC_LEAD_SOURCE is highly correlated with DNA_STD_DC_LEAD_SOURCE_INBOUNDHigh correlation
DAYS_IN_PO_WITH_CHANNEL_C is highly skewed (γ1 = 75.39593925) Skewed
DNA_CUSTOM_DC_CONTACTS_ACTIVE is highly skewed (γ1 = 38.38751239) Skewed
DNA_STD_DC_EVENTS_IA_AFTER_OPPTY_COUNT is highly skewed (γ1 = 25.85647049) Skewed
DNA_STD_DC_EVENTS_IA_BEFORE_OPPTY_COUNT is highly skewed (γ1 = 29.92763112) Skewed
DNA_STD_DC_MKTG_IA_AFTER_OPPTY_COUNT is highly skewed (γ1 = 33.60921849) Skewed
DNA_STD_DC_MKTG_IA_BEFORE_OPPTY_COUNT is highly skewed (γ1 = 45.30960309) Skewed
OPPORTUNITY_ID is uniformly distributed Uniform
ACCOUNT_ID is uniformly distributed Uniform
df_index has unique values Unique
OPPORTUNITY_ID has unique values Unique
DNA_STD_AC_NUMBER_OF_EMPLOYEES has 1037 (5.5%) zeros Zeros
DNA_CUSTOM_DC_PREV_OPPS_COUNT has 13580 (72.0%) zeros Zeros
DNA_CUSTOM_DC_PREV_OPPS_LOST_COUNT has 15075 (79.9%) zeros Zeros
DNA_STD_AC_ANNUAL_REVENUE has 449 (2.4%) zeros Zeros
DAYS_IN_BUSINESS_JUSTIFICATION_C has 16804 (89.1%) zeros Zeros
DAYS_IN_CONSENSUS_C has 14588 (77.4%) zeros Zeros
DAYS_IN_DISCOVERY_C has 11475 (60.9%) zeros Zeros
DAYS_IN_NEGOTIATE_CLOSE_C has 17716 (93.9%) zeros Zeros
DAYS_IN_TECHNICAL_VALIDATION_C has 16458 (87.3%) zeros Zeros
DAYS_IN_PO_WITH_CHANNEL_C has 17711 (93.9%) zeros Zeros
DNA_CUSTOM_DC_DURATION_POC has 18279 (96.9%) zeros Zeros
DNA_STD_DC_MKTG_NURTURE_TIME has 10500 (55.7%) zeros Zeros
DNA_CUSTOM_DC_CONTACTS_ACTIVE has 1056 (5.6%) zeros Zeros
DNA_STD_DC_EVENTS_IA_AFTER_OPPTY_COUNT has 17549 (93.1%) zeros Zeros
DNA_STD_DC_EVENTS_IA_BEFORE_OPPTY_COUNT has 17251 (91.5%) zeros Zeros
DNA_STD_DC_EVENTS_TOTAL_IA_COUNT has 14666 (77.8%) zeros Zeros
DNA_STD_DC_MKTG_IA_AFTER_OPPTY_COUNT has 17171 (91.1%) zeros Zeros
DNA_STD_DC_MKTG_IA_BEFORE_OPPTY_COUNT has 17584 (93.2%) zeros Zeros
DNA_STD_DC_MKTG_TOTAL_IA_COUNT has 14158 (75.1%) zeros Zeros
DNA_STD_DC_TASKS_IA_AFTER_OPPTY_COUNT has 13371 (70.9%) zeros Zeros

Reproduction

Analysis started2021-09-28 23:17:02.851492
Analysis finished2021-09-28 23:17:57.668413
Duration54.82 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct18857
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30071.61155
Minimum5
Maximum56321
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size147.4 KiB
2021-09-29T09:17:57.750184image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile3823.6
Q116117
median31934
Q344067
95-th percentile53049.8
Maximum56321
Range56316
Interquartile range (IQR)27950

Descriptive statistics

Standard deviation15978.15467
Coefficient of variation (CV)0.5313368272
Kurtosis-1.205353892
Mean30071.61155
Median Absolute Deviation (MAD)13879
Skewness-0.1689257834
Sum567060379
Variance255301426.7
MonotonicityStrictly increasing
2021-09-29T09:17:57.855281image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
327681
 
< 0.1%
259101
 
< 0.1%
259421
 
< 0.1%
395131
 
< 0.1%
115991
 
< 0.1%
341221
 
< 0.1%
518601
 
< 0.1%
382161
 
< 0.1%
320691
 
< 0.1%
300201
 
< 0.1%
Other values (18847)18847
99.9%
ValueCountFrequency (%)
51
< 0.1%
81
< 0.1%
91
< 0.1%
141
< 0.1%
181
< 0.1%
201
< 0.1%
211
< 0.1%
221
< 0.1%
231
< 0.1%
241
< 0.1%
ValueCountFrequency (%)
563211
< 0.1%
563201
< 0.1%
563191
< 0.1%
563181
< 0.1%
563171
< 0.1%
563161
< 0.1%
563091
< 0.1%
563081
< 0.1%
563071
< 0.1%
563041
< 0.1%

OPPORTUNITY_ID
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct18857
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size147.4 KiB
0064000000rHRkmAAG
 
1
0066f0000134GWmAAM
 
1
0061W00000ypqr9QAA
 
1
0061W00001EFW0iQAH
 
1
0066f000012ZyzSAAS
 
1
Other values (18852)
18852 

Length

Max length18
Median length18
Mean length18
Min length18

Characters and Unicode

Total characters339426
Distinct characters62
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique18857 ?
Unique (%)100.0%

Sample

1st row0064000000g9NB0AAM
2nd row0064000000g9XLjAAM
3rd row0066f000012ZxZYAA0
4th row0061W000014Qz7yQAC
5th row0064000000g9XMwAAM

Common Values

ValueCountFrequency (%)
0064000000rHRkmAAG1
 
< 0.1%
0066f0000134GWmAAM1
 
< 0.1%
0061W00000ypqr9QAA1
 
< 0.1%
0061W00001EFW0iQAH1
 
< 0.1%
0066f000012ZyzSAAS1
 
< 0.1%
0061W00001FGK6fQAH1
 
< 0.1%
0061W000015c8lxQAA1
 
< 0.1%
0061W00001QNHIbQAP1
 
< 0.1%
0061W00000vaOp7QAE1
 
< 0.1%
0061W00000x8FRJQA21
 
< 0.1%
Other values (18847)18847
99.9%

Length

2021-09-29T09:17:58.062197image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0061w00000yhvsdqam1
 
< 0.1%
0061w00001a1sxgqan1
 
< 0.1%
0064000000qqdaaaa41
 
< 0.1%
0061w00000yb31hqaq1
 
< 0.1%
0064000000lafzhaao1
 
< 0.1%
0064000000lutf7aao1
 
< 0.1%
0061w00000xiyjkqag1
 
< 0.1%
0061w00000txyzbqaa1
 
< 0.1%
0064000000iuglaaae1
 
< 0.1%
0061w000012z66xqas1
 
< 0.1%
Other values (18847)18847
99.9%

Most occurring characters

ValueCountFrequency (%)
0127530
37.6%
A28041
 
8.3%
126251
 
7.7%
622819
 
6.7%
Q15213
 
4.5%
W14553
 
4.3%
46772
 
2.0%
f3329
 
1.0%
23296
 
1.0%
S2810
 
0.8%
Other values (52)88812
26.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number195812
57.7%
Uppercase Letter103430
30.5%
Lowercase Letter40184
 
11.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
f3329
 
8.3%
q2102
 
5.2%
z1919
 
4.8%
y1870
 
4.7%
r1758
 
4.4%
p1722
 
4.3%
x1706
 
4.2%
o1601
 
4.0%
n1589
 
4.0%
m1533
 
3.8%
Other values (16)21055
52.4%
Uppercase Letter
ValueCountFrequency (%)
A28041
27.1%
Q15213
14.7%
W14553
14.1%
S2810
 
2.7%
E2774
 
2.7%
C2667
 
2.6%
I2635
 
2.5%
G2527
 
2.4%
K2188
 
2.1%
Y2184
 
2.1%
Other values (16)27838
26.9%
Decimal Number
ValueCountFrequency (%)
0127530
65.1%
126251
 
13.4%
622819
 
11.7%
46772
 
3.5%
23296
 
1.7%
32399
 
1.2%
51834
 
0.9%
71720
 
0.9%
81643
 
0.8%
91548
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
Common195812
57.7%
Latin143614
42.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
A28041
19.5%
Q15213
 
10.6%
W14553
 
10.1%
f3329
 
2.3%
S2810
 
2.0%
E2774
 
1.9%
C2667
 
1.9%
I2635
 
1.8%
G2527
 
1.8%
K2188
 
1.5%
Other values (42)66877
46.6%
Common
ValueCountFrequency (%)
0127530
65.1%
126251
 
13.4%
622819
 
11.7%
46772
 
3.5%
23296
 
1.7%
32399
 
1.2%
51834
 
0.9%
71720
 
0.9%
81643
 
0.8%
91548
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII339426
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0127530
37.6%
A28041
 
8.3%
126251
 
7.7%
622819
 
6.7%
Q15213
 
4.5%
W14553
 
4.3%
46772
 
2.0%
f3329
 
1.0%
23296
 
1.0%
S2810
 
0.8%
Other values (52)88812
26.2%

ACCOUNT_ID
Categorical

HIGH CARDINALITY
UNIFORM

Distinct14468
Distinct (%)76.7%
Missing0
Missing (%)0.0%
Memory size147.4 KiB
0014000001i13PuAAI
 
16
0014000001n7h4JAAQ
 
15
0014000001m4CMaAAM
 
14
0014000001o73b3AAA
 
13
00140000018iE65AAE
 
12
Other values (14463)
18787 

Length

Max length18
Median length18
Mean length18
Min length18

Characters and Unicode

Total characters339426
Distinct characters62
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11445 ?
Unique (%)60.7%

Sample

1st row0014000001bs9dUAAQ
2nd row0014000001bsF5mAAE
3rd row0014000001bsF5mAAE
4th row0014000001bsF6xAAE
5th row0014000001bsF6xAAE

Common Values

ValueCountFrequency (%)
0014000001i13PuAAI16
 
0.1%
0014000001n7h4JAAQ15
 
0.1%
0014000001m4CMaAAM14
 
0.1%
0014000001o73b3AAA13
 
0.1%
00140000018iE65AAE12
 
0.1%
0011W000023Ye0cQAC12
 
0.1%
0011W000024VCJHQA411
 
0.1%
0011W00001y0TCYQA210
 
0.1%
0014000001o5KseAAE9
 
< 0.1%
0014000001iazWNAAY9
 
< 0.1%
Other values (14458)18736
99.4%

Length

2021-09-29T09:17:58.265483image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0014000001i13puaai16
 
0.1%
0014000001n7h4jaaq15
 
0.1%
0014000001m4cmaaam14
 
0.1%
0014000001o73b3aaa13
 
0.1%
00140000018ie65aae12
 
0.1%
0011w000023ye0cqac12
 
0.1%
0011w000024vcjhqa411
 
0.1%
0011w00001y0tcyqa210
 
0.1%
0014000001iazwnaay9
 
< 0.1%
0014000001o5kseaae9
 
< 0.1%
Other values (14458)18736
99.4%

Most occurring characters

ValueCountFrequency (%)
0123001
36.2%
145372
 
13.4%
A30313
 
8.9%
Q13819
 
4.1%
W13021
 
3.8%
49794
 
2.9%
27698
 
2.3%
33243
 
1.0%
52848
 
0.8%
I2841
 
0.8%
Other values (52)87476
25.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number198158
58.4%
Uppercase Letter96430
28.4%
Lowercase Letter44838
 
13.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o2639
 
5.9%
y2227
 
5.0%
u2208
 
4.9%
r2177
 
4.9%
v2124
 
4.7%
q2080
 
4.6%
f2064
 
4.6%
z1997
 
4.5%
m1921
 
4.3%
w1905
 
4.2%
Other values (16)23496
52.4%
Uppercase Letter
ValueCountFrequency (%)
A30313
31.4%
Q13819
14.3%
W13021
13.5%
I2841
 
2.9%
E2778
 
2.9%
C2672
 
2.8%
Y2560
 
2.7%
M2542
 
2.6%
G2175
 
2.3%
U2131
 
2.2%
Other values (16)21578
22.4%
Decimal Number
ValueCountFrequency (%)
0123001
62.1%
145372
 
22.9%
49794
 
4.9%
27698
 
3.9%
33243
 
1.6%
52848
 
1.4%
62206
 
1.1%
91611
 
0.8%
71197
 
0.6%
81188
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Common198158
58.4%
Latin141268
41.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
A30313
21.5%
Q13819
 
9.8%
W13021
 
9.2%
I2841
 
2.0%
E2778
 
2.0%
C2672
 
1.9%
o2639
 
1.9%
Y2560
 
1.8%
M2542
 
1.8%
y2227
 
1.6%
Other values (42)65856
46.6%
Common
ValueCountFrequency (%)
0123001
62.1%
145372
 
22.9%
49794
 
4.9%
27698
 
3.9%
33243
 
1.6%
52848
 
1.4%
62206
 
1.1%
91611
 
0.8%
71197
 
0.6%
81188
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII339426
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0123001
36.2%
145372
 
13.4%
A30313
 
8.9%
Q13819
 
4.1%
W13021
 
3.8%
49794
 
2.9%
27698
 
2.3%
33243
 
1.0%
52848
 
0.8%
I2841
 
0.8%
Other values (52)87476
25.8%

DNA_STD_DC_END_RESULT
Boolean

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size18.5 KiB
False
16462 
True
2395 
ValueCountFrequency (%)
False16462
87.3%
True2395
 
12.7%
2021-09-29T09:17:58.317853image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

ADVANCED_PAST_STAGE_1_C
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size18.5 KiB
False
9781 
True
9076 
ValueCountFrequency (%)
False9781
51.9%
True9076
48.1%
2021-09-29T09:17:58.351580image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

ADVANCED_PAST_STAGE_2_C
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size18.5 KiB
False
15231 
True
3626 
ValueCountFrequency (%)
False15231
80.8%
True3626
 
19.2%
2021-09-29T09:17:58.385525image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

DNA_STD_DC_AMOUNT
Real number (ℝ≥0)

Distinct7430
Distinct (%)39.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean261753.5242
Minimum70000
Maximum8421302.64
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size147.4 KiB
2021-09-29T09:17:58.462706image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum70000
5-th percentile80000
Q1113000
median200000
Q3276905
95-th percentile674654.504
Maximum8421302.64
Range8351302.64
Interquartile range (IQR)163905

Descriptive statistics

Standard deviation320648.0785
Coefficient of variation (CV)1.225000044
Kurtosis96.73962293
Mean261753.5242
Median Absolute Deviation (MAD)85000
Skewness7.470251924
Sum4935886205
Variance1.028151903 × 1011
MonotonicityNot monotonic
2021-09-29T09:17:58.569607image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2000001972
 
10.5%
1000001821
 
9.7%
1500001359
 
7.2%
2500001257
 
6.7%
300000536
 
2.8%
500000505
 
2.7%
75000310
 
1.6%
80000298
 
1.6%
400000276
 
1.5%
350000258
 
1.4%
Other values (7420)10265
54.4%
ValueCountFrequency (%)
70000159
0.8%
70000.521
 
< 0.1%
700381
 
< 0.1%
70114.841
 
< 0.1%
70141.011
 
< 0.1%
70147.751
 
< 0.1%
70147.851
 
< 0.1%
70167.381
 
< 0.1%
701721
 
< 0.1%
70199.511
 
< 0.1%
ValueCountFrequency (%)
8421302.641
< 0.1%
6924400.41
< 0.1%
64250001
< 0.1%
62000001
< 0.1%
5935188.681
< 0.1%
5818999.681
< 0.1%
5207027.221
< 0.1%
5092228.11
< 0.1%
50000001
< 0.1%
4998922.51
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size18.5 KiB
False
18812 
True
 
45
ValueCountFrequency (%)
False18812
99.8%
True45
 
0.2%
2021-09-29T09:17:58.630529image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size18.5 KiB
False
9561 
True
9296 
ValueCountFrequency (%)
False9561
50.7%
True9296
49.3%
2021-09-29T09:17:58.663912image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

DNA_STD_AC_NUMBER_OF_EMPLOYEES
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct4606
Distinct (%)24.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28133.28631
Minimum0
Maximum1001608
Zeros1037
Zeros (%)5.5%
Negative0
Negative (%)0.0%
Memory size147.4 KiB
2021-09-29T09:17:58.742768image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1260
median1864
Q312811
95-th percentile141258
Maximum1001608
Range1001608
Interquartile range (IQR)12551

Descriptive statistics

Standard deviation86663.3908
Coefficient of variation (CV)3.080457428
Kurtosis49.29147687
Mean28133.28631
Median Absolute Deviation (MAD)1855
Skewness6.121153201
Sum530509380
Variance7510543305
MonotonicityNot monotonic
2021-09-29T09:17:59.379219image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01037
 
5.5%
2188
 
1.0%
1168
 
0.9%
1000157
 
0.8%
2000147
 
0.8%
500142
 
0.8%
3000124
 
0.7%
300115
 
0.6%
3114
 
0.6%
200108
 
0.6%
Other values (4596)16557
87.8%
ValueCountFrequency (%)
01037
5.5%
1168
 
0.9%
2188
 
1.0%
3114
 
0.6%
469
 
0.4%
579
 
0.4%
661
 
0.3%
780
 
0.4%
841
 
0.2%
925
 
0.1%
ValueCountFrequency (%)
100160845
0.2%
66774812
 
0.1%
6475026
 
< 0.1%
6180001
 
< 0.1%
5966961
 
< 0.1%
5960281
 
< 0.1%
57050317
 
0.1%
56881650
0.3%
5546498
 
< 0.1%
5460021
 
< 0.1%

DNA_CUSTOM_DC_PREV_OPPS_COUNT
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct28
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5028371427
Minimum0
Maximum38
Zeros13580
Zeros (%)72.0%
Negative0
Negative (%)0.0%
Memory size147.4 KiB
2021-09-29T09:17:59.473073image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum38
Range38
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.308649512
Coefficient of variation (CV)2.602531518
Kurtosis162.807401
Mean0.5028371427
Median Absolute Deviation (MAD)0
Skewness9.090605946
Sum9482
Variance1.712563545
MonotonicityNot monotonic
2021-09-29T09:17:59.570169image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
013580
72.0%
13347
 
17.7%
21106
 
5.9%
3393
 
2.1%
4168
 
0.9%
599
 
0.5%
653
 
0.3%
729
 
0.2%
819
 
0.1%
914
 
0.1%
Other values (18)49
 
0.3%
ValueCountFrequency (%)
013580
72.0%
13347
 
17.7%
21106
 
5.9%
3393
 
2.1%
4168
 
0.9%
599
 
0.5%
653
 
0.3%
729
 
0.2%
819
 
0.1%
914
 
0.1%
ValueCountFrequency (%)
381
< 0.1%
352
< 0.1%
311
< 0.1%
291
< 0.1%
281
< 0.1%
241
< 0.1%
221
< 0.1%
211
< 0.1%
192
< 0.1%
181
< 0.1%

DNA_CUSTOM_DC_PREV_OPPS_LOST_COUNT
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3087977939
Minimum0
Maximum16
Zeros15075
Zeros (%)79.9%
Negative0
Negative (%)0.0%
Memory size147.4 KiB
2021-09-29T09:17:59.651583image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum16
Range16
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.7960468743
Coefficient of variation (CV)2.577890419
Kurtosis37.55787031
Mean0.3087977939
Median Absolute Deviation (MAD)0
Skewness4.716968201
Sum5823
Variance0.6336906261
MonotonicityNot monotonic
2021-09-29T09:17:59.734348image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
015075
79.9%
12602
 
13.8%
2756
 
4.0%
3237
 
1.3%
493
 
0.5%
530
 
0.2%
628
 
0.1%
811
 
0.1%
711
 
0.1%
1010
 
0.1%
Other values (2)4
 
< 0.1%
ValueCountFrequency (%)
015075
79.9%
12602
 
13.8%
2756
 
4.0%
3237
 
1.3%
493
 
0.5%
530
 
0.2%
628
 
0.1%
711
 
0.1%
811
 
0.1%
93
 
< 0.1%
ValueCountFrequency (%)
161
 
< 0.1%
1010
 
0.1%
93
 
< 0.1%
811
 
0.1%
711
 
0.1%
628
 
0.1%
530
 
0.2%
493
 
0.5%
3237
 
1.3%
2756
4.0%

DNA_STD_AC_ANNUAL_REVENUE
Real number (ℝ≥0)

ZEROS

Distinct13668
Distinct (%)72.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2013323474
Minimum0
Maximum9.607312 × 1010
Zeros449
Zeros (%)2.4%
Negative0
Negative (%)0.0%
Memory size147.4 KiB
2021-09-29T09:17:59.834317image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile86004.6
Q111424633
median124677687
Q3742508045
95-th percentile9343858269
Maximum9.607312 × 1010
Range9.607312 × 1010
Interquartile range (IQR)731083412

Descriptive statistics

Standard deviation7395377209
Coefficient of variation (CV)3.673218588
Kurtosis54.92472867
Mean2013323474
Median Absolute Deviation (MAD)124077374
Skewness6.825508767
Sum3.796524075 × 1013
Variance5.469160407 × 1019
MonotonicityNot monotonic
2021-09-29T09:17:59.949485image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0449
 
2.4%
9445930
 
0.2%
8557119
 
0.1%
20970018
 
0.1%
5.9812 × 101018
 
0.1%
27862542215
 
0.1%
103294614
 
0.1%
450000013
 
0.1%
6.722570625 × 101013
 
0.1%
7.4286 × 101012
 
0.1%
Other values (13658)18256
96.8%
ValueCountFrequency (%)
0449
2.4%
591
 
< 0.1%
792
 
< 0.1%
1331
 
< 0.1%
1341
 
< 0.1%
3381
 
< 0.1%
6011
 
< 0.1%
6431
 
< 0.1%
6521
 
< 0.1%
9891
 
< 0.1%
ValueCountFrequency (%)
9.607312 × 10101
 
< 0.1%
9.0165186 × 10101
 
< 0.1%
8.9571172 × 10101
 
< 0.1%
8.765429345 × 10103
 
< 0.1%
8.2574 × 101010
0.1%
8.2501797 × 10102
 
< 0.1%
8.201774126 × 10101
 
< 0.1%
7.854315387 × 10102
 
< 0.1%
7.73972963 × 10101
 
< 0.1%
7.7147 × 10102
 
< 0.1%

DAYS_IN_BUSINESS_JUSTIFICATION_C
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct1633
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.53336692
Minimum0
Maximum1378.83
Zeros16804
Zeros (%)89.1%
Negative0
Negative (%)0.0%
Memory size147.4 KiB
2021-09-29T09:18:00.053886image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile74.43
Maximum1378.83
Range1378.83
Interquartile range (IQR)0

Descriptive statistics

Standard deviation70.65134113
Coefficient of variation (CV)4.861319578
Kurtosis69.40887601
Mean14.53336692
Median Absolute Deviation (MAD)0
Skewness7.404857645
Sum274055.7
Variance4991.612004
MonotonicityNot monotonic
2021-09-29T09:18:00.164759image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
016804
89.1%
0.0410
 
0.1%
0.087
 
< 0.1%
30.926
 
< 0.1%
7.046
 
< 0.1%
0.175
 
< 0.1%
28.085
 
< 0.1%
2.755
 
< 0.1%
6.135
 
< 0.1%
6.965
 
< 0.1%
Other values (1623)1999
 
10.6%
ValueCountFrequency (%)
016804
89.1%
0.0410
 
0.1%
0.087
 
< 0.1%
0.135
 
< 0.1%
0.175
 
< 0.1%
0.215
 
< 0.1%
0.252
 
< 0.1%
0.291
 
< 0.1%
0.332
 
< 0.1%
0.381
 
< 0.1%
ValueCountFrequency (%)
1378.831
< 0.1%
1246.081
< 0.1%
1207.381
< 0.1%
1067.791
< 0.1%
1022.171
< 0.1%
986.831
< 0.1%
886.171
< 0.1%
862.211
< 0.1%
850.791
< 0.1%
846.581
< 0.1%

DAYS_IN_CONSENSUS_C
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct3210
Distinct (%)17.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.2983937
Minimum0
Maximum1541.96
Zeros14588
Zeros (%)77.4%
Negative0
Negative (%)0.0%
Memory size147.4 KiB
2021-09-29T09:18:00.270023image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile324.342
Maximum1541.96
Range1541.96
Interquartile range (IQR)0

Descriptive statistics

Standard deviation138.8827975
Coefficient of variation (CV)2.999732527
Kurtosis21.65576034
Mean46.2983937
Median Absolute Deviation (MAD)0
Skewness4.25155337
Sum873048.81
Variance19288.43144
MonotonicityNot monotonic
2021-09-29T09:18:00.384045image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
014588
77.4%
15.4231
 
0.2%
15.4627
 
0.1%
15.5426
 
0.1%
15.519
 
0.1%
15.2917
 
0.1%
15.3316
 
0.1%
15.5814
 
0.1%
15.7113
 
0.1%
15.2112
 
0.1%
Other values (3200)4094
 
21.7%
ValueCountFrequency (%)
014588
77.4%
0.045
 
< 0.1%
0.082
 
< 0.1%
0.137
 
< 0.1%
0.173
 
< 0.1%
0.211
 
< 0.1%
0.251
 
< 0.1%
0.331
 
< 0.1%
0.382
 
< 0.1%
0.421
 
< 0.1%
ValueCountFrequency (%)
1541.961
< 0.1%
1505.381
< 0.1%
1439.921
< 0.1%
1437.881
< 0.1%
1435.251
< 0.1%
1368.921
< 0.1%
1341.961
< 0.1%
1331.671
< 0.1%
1312.791
< 0.1%
1275.291
< 0.1%

DAYS_IN_DISCOVERY_C
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct4859
Distinct (%)25.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean78.41969454
Minimum0
Maximum3257.25
Zeros11475
Zeros (%)60.9%
Negative0
Negative (%)0.0%
Memory size147.4 KiB
2021-09-29T09:18:00.492594image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q370.92
95-th percentile440.96
Maximum3257.25
Range3257.25
Interquartile range (IQR)70.92

Descriptive statistics

Standard deviation166.2224635
Coefficient of variation (CV)2.119652015
Kurtosis21.18547811
Mean78.41969454
Median Absolute Deviation (MAD)0
Skewness3.427995121
Sum1478760.18
Variance27629.90738
MonotonicityNot monotonic
2021-09-29T09:18:00.601840image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
011475
60.9%
0.0431
 
0.2%
60.9625
 
0.1%
61.524
 
0.1%
61.4622
 
0.1%
0.0819
 
0.1%
61.3319
 
0.1%
61.5418
 
0.1%
0.9616
 
0.1%
61.2916
 
0.1%
Other values (4849)7192
38.1%
ValueCountFrequency (%)
011475
60.9%
0.0431
 
0.2%
0.0819
 
0.1%
0.1313
 
0.1%
0.1710
 
0.1%
0.219
 
< 0.1%
0.258
 
< 0.1%
0.298
 
< 0.1%
0.333
 
< 0.1%
0.383
 
< 0.1%
ValueCountFrequency (%)
3257.251
< 0.1%
2459.751
< 0.1%
22591
< 0.1%
2133.251
< 0.1%
18961
< 0.1%
1599.131
< 0.1%
1553.961
< 0.1%
14851
< 0.1%
1457.041
< 0.1%
1391.881
< 0.1%

DAYS_IN_NEGOTIATE_CLOSE_C
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct732
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.954315108
Minimum0
Maximum1104.67
Zeros17716
Zeros (%)93.9%
Negative0
Negative (%)0.0%
Memory size147.4 KiB
2021-09-29T09:18:00.708162image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2.54
Maximum1104.67
Range1104.67
Interquartile range (IQR)0

Descriptive statistics

Standard deviation26.62341945
Coefficient of variation (CV)9.01170609
Kurtosis391.5714198
Mean2.954315108
Median Absolute Deviation (MAD)0
Skewness16.88581509
Sum55709.52
Variance708.8064634
MonotonicityNot monotonic
2021-09-29T09:18:00.818114image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
017716
93.9%
0.1316
 
0.1%
0.0412
 
0.1%
0.0812
 
0.1%
0.1710
 
0.1%
0.9210
 
0.1%
1.049
 
< 0.1%
0.798
 
< 0.1%
16
 
< 0.1%
0.886
 
< 0.1%
Other values (722)1052
 
5.6%
ValueCountFrequency (%)
017716
93.9%
0.0412
 
0.1%
0.0812
 
0.1%
0.1316
 
0.1%
0.1710
 
0.1%
0.216
 
< 0.1%
0.256
 
< 0.1%
0.291
 
< 0.1%
0.332
 
< 0.1%
0.424
 
< 0.1%
ValueCountFrequency (%)
1104.671
< 0.1%
765.421
< 0.1%
685.041
< 0.1%
654.131
< 0.1%
6181
< 0.1%
610.791
< 0.1%
6001
< 0.1%
559.211
< 0.1%
558.211
< 0.1%
542.921
< 0.1%

DAYS_IN_TECHNICAL_VALIDATION_C
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct1948
Distinct (%)10.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.64047781
Minimum0
Maximum1559.17
Zeros16458
Zeros (%)87.3%
Negative0
Negative (%)0.0%
Memory size147.4 KiB
2021-09-29T09:18:00.923961image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile127.052
Maximum1559.17
Range1559.17
Interquartile range (IQR)0

Descriptive statistics

Standard deviation88.86063171
Coefficient of variation (CV)4.305163502
Kurtosis53.61278632
Mean20.64047781
Median Absolute Deviation (MAD)0
Skewness6.498707814
Sum389217.49
Variance7896.211867
MonotonicityNot monotonic
2021-09-29T09:18:01.039265image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
016458
87.3%
0.049
 
< 0.1%
6.927
 
< 0.1%
6.966
 
< 0.1%
42.926
 
< 0.1%
146
 
< 0.1%
7.085
 
< 0.1%
12.924
 
< 0.1%
13.674
 
< 0.1%
1.674
 
< 0.1%
Other values (1938)2348
 
12.5%
ValueCountFrequency (%)
016458
87.3%
0.049
 
< 0.1%
0.084
 
< 0.1%
0.133
 
< 0.1%
0.172
 
< 0.1%
0.212
 
< 0.1%
0.251
 
< 0.1%
0.291
 
< 0.1%
0.333
 
< 0.1%
0.423
 
< 0.1%
ValueCountFrequency (%)
1559.171
< 0.1%
1339.211
< 0.1%
1309.881
< 0.1%
1245.421
< 0.1%
1239.381
< 0.1%
1203.461
< 0.1%
1181.671
< 0.1%
1134.211
< 0.1%
1119.041
< 0.1%
1114.751
< 0.1%

DAYS_IN_PO_WITH_CHANNEL_C
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct262
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2567386117
Minimum0
Maximum419.29
Zeros17711
Zeros (%)93.9%
Negative0
Negative (%)0.0%
Memory size147.4 KiB
2021-09-29T09:18:01.150810image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.25
Maximum419.29
Range419.29
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4.25987279
Coefficient of variation (CV)16.59225608
Kurtosis6773.133279
Mean0.2567386117
Median Absolute Deviation (MAD)0
Skewness75.39593925
Sum4841.32
Variance18.14651619
MonotonicityNot monotonic
2021-09-29T09:18:01.256465image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
017711
93.9%
0.2555
 
0.3%
0.1745
 
0.2%
0.0843
 
0.2%
0.0434
 
0.2%
0.1332
 
0.2%
0.3330
 
0.2%
0.4229
 
0.2%
0.2122
 
0.1%
122
 
0.1%
Other values (252)834
 
4.4%
ValueCountFrequency (%)
017711
93.9%
0.0434
 
0.2%
0.0843
 
0.2%
0.1332
 
0.2%
0.1745
 
0.2%
0.2122
 
0.1%
0.2555
 
0.3%
0.2918
 
0.1%
0.3330
 
0.2%
0.3812
 
0.1%
ValueCountFrequency (%)
419.291
< 0.1%
325.041
< 0.1%
77.041
< 0.1%
70.831
< 0.1%
53.291
< 0.1%
50.251
< 0.1%
48.831
< 0.1%
43.631
< 0.1%
40.921
< 0.1%
37.581
< 0.1%

DNA_CUSTOM_DC_DURATION_POC
Real number (ℝ)

ZEROS

Distinct125
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.294691626
Minimum-18
Maximum479
Zeros18279
Zeros (%)96.9%
Negative3
Negative (%)< 0.1%
Memory size147.4 KiB
2021-09-29T09:18:01.358605image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-18
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum479
Range497
Interquartile range (IQR)0

Descriptive statistics

Standard deviation11.90578156
Coefficient of variation (CV)9.195843489
Kurtosis503.3585826
Mean1.294691626
Median Absolute Deviation (MAD)0
Skewness18.73553191
Sum24414
Variance141.7476346
MonotonicityNot monotonic
2021-09-29T09:18:01.474219image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
018279
96.9%
1444
 
0.2%
1127
 
0.1%
1821
 
0.1%
720
 
0.1%
1617
 
0.1%
2815
 
0.1%
1015
 
0.1%
3015
 
0.1%
2115
 
0.1%
Other values (115)389
 
2.1%
ValueCountFrequency (%)
-181
 
< 0.1%
-61
 
< 0.1%
-11
 
< 0.1%
018279
96.9%
19
 
< 0.1%
23
 
< 0.1%
32
 
< 0.1%
414
 
0.1%
51
 
< 0.1%
64
 
< 0.1%
ValueCountFrequency (%)
4791
< 0.1%
3961
< 0.1%
3821
< 0.1%
3791
< 0.1%
3761
< 0.1%
3611
< 0.1%
3281
< 0.1%
2621
< 0.1%
2161
< 0.1%
1802
< 0.1%

DNA_STD_DC_MKTG_NURTURE_TIME
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct1506
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean207.8628626
Minimum-682
Maximum2186
Zeros10500
Zeros (%)55.7%
Negative102
Negative (%)0.5%
Memory size147.4 KiB
2021-09-29T09:18:01.577444image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-682
5-th percentile0
Q10
median0
Q3336
95-th percentile983
Maximum2186
Range2868
Interquartile range (IQR)336

Descriptive statistics

Standard deviation346.6097347
Coefficient of variation (CV)1.667492357
Kurtosis2.957007145
Mean207.8628626
Median Absolute Deviation (MAD)0
Skewness1.829307947
Sum3919670
Variance120138.3082
MonotonicityNot monotonic
2021-09-29T09:18:01.683188image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
010500
55.7%
1154
 
0.8%
257
 
0.3%
353
 
0.3%
443
 
0.2%
641
 
0.2%
734
 
0.2%
534
 
0.2%
831
 
0.2%
1423
 
0.1%
Other values (1496)7887
41.8%
ValueCountFrequency (%)
-6821
< 0.1%
-5401
< 0.1%
-5031
< 0.1%
-4481
< 0.1%
-4071
< 0.1%
-4041
< 0.1%
-3781
< 0.1%
-3751
< 0.1%
-3291
< 0.1%
-3161
< 0.1%
ValueCountFrequency (%)
21861
< 0.1%
21221
< 0.1%
20721
< 0.1%
20251
< 0.1%
20091
< 0.1%
19731
< 0.1%
19601
< 0.1%
19361
< 0.1%
19271
< 0.1%
19211
< 0.1%

DNA_CUSTOM_DC_CONTACTS_ACTIVE
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct117
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.497003765
Minimum0
Maximum1420
Zeros1056
Zeros (%)5.6%
Negative0
Negative (%)0.0%
Memory size147.4 KiB
2021-09-29T09:18:01.785877image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q35
95-th percentile20
Maximum1420
Range1420
Interquartile range (IQR)4

Descriptive statistics

Standard deviation16.72259262
Coefficient of variation (CV)3.042128646
Kurtosis2823.327878
Mean5.497003765
Median Absolute Deviation (MAD)1
Skewness38.38751239
Sum103657
Variance279.645104
MonotonicityNot monotonic
2021-09-29T09:18:01.896570image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16192
32.8%
23683
19.5%
31800
 
9.5%
41215
 
6.4%
01056
 
5.6%
5764
 
4.1%
6544
 
2.9%
7427
 
2.3%
8355
 
1.9%
9306
 
1.6%
Other values (107)2515
13.3%
ValueCountFrequency (%)
01056
 
5.6%
16192
32.8%
23683
19.5%
31800
 
9.5%
41215
 
6.4%
5764
 
4.1%
6544
 
2.9%
7427
 
2.3%
8355
 
1.9%
9306
 
1.6%
ValueCountFrequency (%)
14201
< 0.1%
4591
< 0.1%
4091
< 0.1%
3521
< 0.1%
3511
< 0.1%
3301
< 0.1%
3122
< 0.1%
3051
< 0.1%
3001
< 0.1%
2101
< 0.1%

DNA_STD_DC_EVENTS_IA_AFTER_OPPTY_COUNT
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct50
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.301956833
Minimum0
Maximum161
Zeros17549
Zeros (%)93.1%
Negative0
Negative (%)0.0%
Memory size147.4 KiB
2021-09-29T09:18:02.004398image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum161
Range161
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.622463599
Coefficient of variation (CV)8.684895694
Kurtosis1061.597819
Mean0.301956833
Median Absolute Deviation (MAD)0
Skewness25.85647049
Sum5694
Variance6.877315326
MonotonicityNot monotonic
2021-09-29T09:18:02.119121image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
017549
93.1%
1514
 
2.7%
2331
 
1.8%
3118
 
0.6%
472
 
0.4%
656
 
0.3%
542
 
0.2%
727
 
0.1%
916
 
0.1%
815
 
0.1%
Other values (40)117
 
0.6%
ValueCountFrequency (%)
017549
93.1%
1514
 
2.7%
2331
 
1.8%
3118
 
0.6%
472
 
0.4%
542
 
0.2%
656
 
0.3%
727
 
0.1%
815
 
0.1%
916
 
0.1%
ValueCountFrequency (%)
1611
 
< 0.1%
832
< 0.1%
801
 
< 0.1%
761
 
< 0.1%
721
 
< 0.1%
671
 
< 0.1%
521
 
< 0.1%
484
< 0.1%
471
 
< 0.1%
461
 
< 0.1%

DNA_STD_DC_EVENTS_IA_BEFORE_OPPTY_COUNT
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct28
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2059182267
Minimum0
Maximum87
Zeros17251
Zeros (%)91.5%
Negative0
Negative (%)0.0%
Memory size147.4 KiB
2021-09-29T09:18:02.217164image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum87
Range87
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.351499025
Coefficient of variation (CV)6.563280225
Kurtosis1530.283876
Mean0.2059182267
Median Absolute Deviation (MAD)0
Skewness29.92763112
Sum3883
Variance1.826549614
MonotonicityNot monotonic
2021-09-29T09:18:02.308156image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
017251
91.5%
1801
 
4.2%
2456
 
2.4%
3114
 
0.6%
481
 
0.4%
547
 
0.2%
637
 
0.2%
814
 
0.1%
712
 
0.1%
128
 
< 0.1%
Other values (18)36
 
0.2%
ValueCountFrequency (%)
017251
91.5%
1801
 
4.2%
2456
 
2.4%
3114
 
0.6%
481
 
0.4%
547
 
0.2%
637
 
0.2%
712
 
0.1%
814
 
0.1%
92
 
< 0.1%
ValueCountFrequency (%)
871
< 0.1%
761
< 0.1%
361
< 0.1%
291
< 0.1%
272
< 0.1%
261
< 0.1%
251
< 0.1%
232
< 0.1%
222
< 0.1%
202
< 0.1%

DNA_STD_DC_EVENTS_TOTAL_IA_COUNT
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct62
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.191440844
Minimum0
Maximum240
Zeros14666
Zeros (%)77.8%
Negative0
Negative (%)0.0%
Memory size147.4 KiB
2021-09-29T09:18:02.403644image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile5
Maximum240
Range240
Interquartile range (IQR)0

Descriptive statistics

Standard deviation6.356393536
Coefficient of variation (CV)5.335047532
Kurtosis450.8738593
Mean1.191440844
Median Absolute Deviation (MAD)0
Skewness17.99864448
Sum22467
Variance40.40373879
MonotonicityNot monotonic
2021-09-29T09:18:02.517994image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
014666
77.8%
11209
 
6.4%
21002
 
5.3%
3474
 
2.5%
4385
 
2.0%
5212
 
1.1%
6189
 
1.0%
8132
 
0.7%
7102
 
0.5%
965
 
0.3%
Other values (52)421
 
2.2%
ValueCountFrequency (%)
014666
77.8%
11209
 
6.4%
21002
 
5.3%
3474
 
2.5%
4385
 
2.0%
5212
 
1.1%
6189
 
1.0%
7102
 
0.5%
8132
 
0.7%
965
 
0.3%
ValueCountFrequency (%)
2402
 
< 0.1%
1711
 
< 0.1%
14012
0.1%
1393
 
< 0.1%
1241
 
< 0.1%
1171
 
< 0.1%
1021
 
< 0.1%
901
 
< 0.1%
843
 
< 0.1%
771
 
< 0.1%

DNA_STD_DC_MKTG_IA_AFTER_OPPTY_COUNT
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct74
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7511269025
Minimum0
Maximum430
Zeros17171
Zeros (%)91.1%
Negative0
Negative (%)0.0%
Memory size147.4 KiB
2021-09-29T09:18:02.625785image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum430
Range430
Interquartile range (IQR)0

Descriptive statistics

Standard deviation7.917093472
Coefficient of variation (CV)10.54028746
Kurtosis1422.454074
Mean0.7511269025
Median Absolute Deviation (MAD)0
Skewness33.60921849
Sum14164
Variance62.68036904
MonotonicityNot monotonic
2021-09-29T09:18:02.734142image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
017171
91.1%
1434
 
2.3%
2335
 
1.8%
3156
 
0.8%
4150
 
0.8%
598
 
0.5%
670
 
0.4%
849
 
0.3%
748
 
0.3%
938
 
0.2%
Other values (64)308
 
1.6%
ValueCountFrequency (%)
017171
91.1%
1434
 
2.3%
2335
 
1.8%
3156
 
0.8%
4150
 
0.8%
598
 
0.5%
670
 
0.4%
748
 
0.3%
849
 
0.3%
938
 
0.2%
ValueCountFrequency (%)
4301
< 0.1%
3591
< 0.1%
3401
< 0.1%
3371
< 0.1%
3331
< 0.1%
3141
< 0.1%
2511
< 0.1%
1661
< 0.1%
1531
< 0.1%
1421
< 0.1%

DNA_STD_DC_MKTG_IA_BEFORE_OPPTY_COUNT
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct52
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4642838203
Minimum0
Maximum551
Zeros17584
Zeros (%)93.2%
Negative0
Negative (%)0.0%
Memory size147.4 KiB
2021-09-29T09:18:02.835708image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum551
Range551
Interquartile range (IQR)0

Descriptive statistics

Standard deviation7.920459332
Coefficient of variation (CV)17.05952046
Kurtosis2431.192844
Mean0.4642838203
Median Absolute Deviation (MAD)0
Skewness45.30960309
Sum8755
Variance62.73367603
MonotonicityNot monotonic
2021-09-29T09:18:02.944188image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
017584
93.2%
1423
 
2.2%
2300
 
1.6%
3123
 
0.7%
4109
 
0.6%
581
 
0.4%
653
 
0.3%
725
 
0.1%
921
 
0.1%
818
 
0.1%
Other values (42)120
 
0.6%
ValueCountFrequency (%)
017584
93.2%
1423
 
2.2%
2300
 
1.6%
3123
 
0.7%
4109
 
0.6%
581
 
0.4%
653
 
0.3%
725
 
0.1%
818
 
0.1%
921
 
0.1%
ValueCountFrequency (%)
5511
< 0.1%
3862
< 0.1%
3841
< 0.1%
3201
< 0.1%
3131
< 0.1%
1811
< 0.1%
1371
< 0.1%
1292
< 0.1%
1111
< 0.1%
1101
< 0.1%

DNA_STD_DC_MKTG_TOTAL_IA_COUNT
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct156
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.879726362
Minimum0
Maximum1001
Zeros14158
Zeros (%)75.1%
Negative0
Negative (%)0.0%
Memory size147.4 KiB
2021-09-29T09:18:03.050938image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile22
Maximum1001
Range1001
Interquartile range (IQR)0

Descriptive statistics

Standard deviation27.52673058
Coefficient of variation (CV)5.641039791
Kurtosis532.9154415
Mean4.879726362
Median Absolute Deviation (MAD)0
Skewness19.3184144
Sum92017
Variance757.7208962
MonotonicityNot monotonic
2021-09-29T09:18:03.163166image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
014158
75.1%
1756
 
4.0%
2577
 
3.1%
3384
 
2.0%
4353
 
1.9%
5241
 
1.3%
6205
 
1.1%
7144
 
0.8%
8140
 
0.7%
9122
 
0.6%
Other values (146)1777
 
9.4%
ValueCountFrequency (%)
014158
75.1%
1756
 
4.0%
2577
 
3.1%
3384
 
2.0%
4353
 
1.9%
5241
 
1.3%
6205
 
1.1%
7144
 
0.8%
8140
 
0.7%
9122
 
0.6%
ValueCountFrequency (%)
10012
 
< 0.1%
9541
 
< 0.1%
9491
 
< 0.1%
8241
 
< 0.1%
7532
 
< 0.1%
7042
 
< 0.1%
4591
 
< 0.1%
4518
< 0.1%
4492
 
< 0.1%
4433
 
< 0.1%

DNA_STD_DC_TASKS_IA_AFTER_OPPTY_COUNT
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct182
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.329957045
Minimum0
Maximum1065
Zeros13371
Zeros (%)70.9%
Negative0
Negative (%)0.0%
Memory size147.4 KiB
2021-09-29T09:18:03.268644image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile20
Maximum1065
Range1065
Interquartile range (IQR)1

Descriptive statistics

Standard deviation22.00360479
Coefficient of variation (CV)5.081714335
Kurtosis607.2304429
Mean4.329957045
Median Absolute Deviation (MAD)0
Skewness19.38813218
Sum81650
Variance484.1586236
MonotonicityNot monotonic
2021-09-29T09:18:03.378618image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
013371
70.9%
11248
 
6.6%
2631
 
3.3%
3413
 
2.2%
4359
 
1.9%
5263
 
1.4%
6221
 
1.2%
7202
 
1.1%
8146
 
0.8%
9142
 
0.8%
Other values (172)1861
 
9.9%
ValueCountFrequency (%)
013371
70.9%
11248
 
6.6%
2631
 
3.3%
3413
 
2.2%
4359
 
1.9%
5263
 
1.4%
6221
 
1.2%
7202
 
1.1%
8146
 
0.8%
9142
 
0.8%
ValueCountFrequency (%)
10651
< 0.1%
8621
< 0.1%
6391
< 0.1%
5941
< 0.1%
5911
< 0.1%
5331
< 0.1%
5061
< 0.1%
4641
< 0.1%
4431
< 0.1%
4281
< 0.1%

DNA_ML_OPPORTUNITY_LIFE_DAYS
Real number (ℝ)

HIGH CORRELATION

Distinct1042
Distinct (%)5.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean253.4450867
Minimum-1
Maximum1593
Zeros33
Zeros (%)0.2%
Negative58
Negative (%)0.3%
Memory size147.4 KiB
2021-09-29T09:18:03.484938image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile27
Q199
median199
Q3354
95-th percentile670
Maximum1593
Range1594
Interquartile range (IQR)255

Descriptive statistics

Standard deviation207.0549505
Coefficient of variation (CV)0.8169617851
Kurtosis2.575246693
Mean253.4450867
Median Absolute Deviation (MAD)115
Skewness1.427736347
Sum4779214
Variance42871.75251
MonotonicityNot monotonic
2021-09-29T09:18:03.599066image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8988
 
0.5%
10486
 
0.5%
6282
 
0.4%
5580
 
0.4%
9080
 
0.4%
12578
 
0.4%
11172
 
0.4%
12172
 
0.4%
6172
 
0.4%
5470
 
0.4%
Other values (1032)18077
95.9%
ValueCountFrequency (%)
-158
0.3%
033
0.2%
130
0.2%
226
0.1%
321
 
0.1%
423
 
0.1%
537
0.2%
635
0.2%
733
0.2%
828
0.1%
ValueCountFrequency (%)
15931
< 0.1%
15571
< 0.1%
15531
< 0.1%
15401
< 0.1%
15391
< 0.1%
15021
< 0.1%
14561
< 0.1%
14001
< 0.1%
13911
< 0.1%
13821
< 0.1%

SUB_REGION_C
Categorical

HIGH CARDINALITY
HIGH CORRELATION

Distinct76
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size147.4 KiB
Unknown
 
1327
SoCal
 
836
Bay Area
 
814
NYNJ
 
810
UK South
 
803
Other values (71)
14267 

Length

Max length16
Median length8
Mean length8.742270775
Min length2

Characters and Unicode

Total characters164853
Distinct characters53
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowEast
2nd rowUnknown
3rd rowS. TOLA
4th rowOhio Valley OHVA
5th rowUnknown

Common Values

ValueCountFrequency (%)
Unknown1327
 
7.0%
SoCal836
 
4.4%
Bay Area814
 
4.3%
NYNJ810
 
4.3%
UK South803
 
4.3%
Carolinas/TN773
 
4.1%
Mid Atlantic697
 
3.7%
Great Plains692
 
3.7%
France689
 
3.7%
New England670
 
3.6%
Other values (66)10746
57.0%

Length

2021-09-29T09:18:03.826016image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
uk1488
 
5.4%
unknown1327
 
4.8%
tola1186
 
4.3%
south862
 
3.1%
socal836
 
3.0%
area814
 
2.9%
bay814
 
2.9%
nynj810
 
2.9%
great796
 
2.9%
carolinas/tn773
 
2.8%
Other values (79)18011
65.0%

Most occurring characters

ValueCountFrequency (%)
a15110
 
9.2%
n11161
 
6.8%
e8967
 
5.4%
8860
 
5.4%
o8766
 
5.3%
l7624
 
4.6%
t7447
 
4.5%
i7394
 
4.5%
N7046
 
4.3%
r6864
 
4.2%
Other values (43)75614
45.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter103206
62.6%
Uppercase Letter48421
29.4%
Space Separator8860
 
5.4%
Other Punctuation4327
 
2.6%
Dash Punctuation36
 
< 0.1%
Decimal Number3
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N7046
14.6%
A5876
 
12.1%
S4319
 
8.9%
C3205
 
6.6%
U2938
 
6.1%
L2378
 
4.9%
T2064
 
4.3%
O1949
 
4.0%
P1631
 
3.4%
M1563
 
3.2%
Other values (14)15452
31.9%
Lowercase Letter
ValueCountFrequency (%)
a15110
14.6%
n11161
10.8%
e8967
8.7%
o8766
8.5%
l7624
 
7.4%
t7447
 
7.2%
i7394
 
7.2%
r6864
 
6.7%
s4679
 
4.5%
d4353
 
4.2%
Other values (13)20841
20.2%
Other Punctuation
ValueCountFrequency (%)
/2143
49.5%
.1137
26.3%
&1047
24.2%
Space Separator
ValueCountFrequency (%)
8860
100.0%
Dash Punctuation
ValueCountFrequency (%)
-36
100.0%
Decimal Number
ValueCountFrequency (%)
33
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin151627
92.0%
Common13226
 
8.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a15110
 
10.0%
n11161
 
7.4%
e8967
 
5.9%
o8766
 
5.8%
l7624
 
5.0%
t7447
 
4.9%
i7394
 
4.9%
N7046
 
4.6%
r6864
 
4.5%
A5876
 
3.9%
Other values (37)65372
43.1%
Common
ValueCountFrequency (%)
8860
67.0%
/2143
 
16.2%
.1137
 
8.6%
&1047
 
7.9%
-36
 
0.3%
33
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII164853
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a15110
 
9.2%
n11161
 
6.8%
e8967
 
5.4%
8860
 
5.4%
o8766
 
5.3%
l7624
 
4.6%
t7447
 
4.5%
i7394
 
4.5%
N7046
 
4.3%
r6864
 
4.2%
Other values (43)75614
45.9%

OWNER_SUB_REGION_C
Categorical

HIGH CARDINALITY

Distinct144
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size147.4 KiB
Unknown
 
1327
SoCal
 
829
UK South
 
816
NYNJ
 
807
Bay Area
 
801
Other values (139)
14277 

Length

Max length16
Median length8
Mean length8.79455905
Min length2

Characters and Unicode

Total characters165839
Distinct characters60
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique43 ?
Unique (%)0.2%

Sample

1st rowEast
2nd rowUnknown
3rd rowS. TOLA
4th rowOhio Valley OHVA
5th rowUnknown

Common Values

ValueCountFrequency (%)
Unknown1327
 
7.0%
SoCal829
 
4.4%
UK South816
 
4.3%
NYNJ807
 
4.3%
Bay Area801
 
4.2%
Carolinas/TN760
 
4.0%
Great Plains716
 
3.8%
Mid Atlantic690
 
3.7%
Florida669
 
3.5%
New England659
 
3.5%
Other values (134)10783
57.2%

Length

2021-09-29T09:18:04.042016image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
uk1484
 
5.3%
unknown1327
 
4.8%
tola1177
 
4.2%
south875
 
3.2%
socal829
 
3.0%
nynj807
 
2.9%
bay801
 
2.9%
area801
 
2.9%
great770
 
2.8%
carolinas/tn760
 
2.7%
Other values (147)18145
65.3%

Most occurring characters

ValueCountFrequency (%)
a14971
 
9.0%
n11004
 
6.6%
8919
 
5.4%
o8811
 
5.3%
e8800
 
5.3%
l7611
 
4.6%
t7523
 
4.5%
i7348
 
4.4%
N7044
 
4.2%
r6870
 
4.1%
Other values (50)76938
46.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter102453
61.8%
Uppercase Letter49516
29.9%
Space Separator8919
 
5.4%
Other Punctuation4393
 
2.6%
Dash Punctuation371
 
0.2%
Decimal Number187
 
0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N7044
14.2%
A6054
 
12.2%
S4539
 
9.2%
C3234
 
6.5%
U2945
 
5.9%
L2392
 
4.8%
T2127
 
4.3%
O2073
 
4.2%
P1681
 
3.4%
I1615
 
3.3%
Other values (15)15812
31.9%
Lowercase Letter
ValueCountFrequency (%)
a14971
14.6%
n11004
10.7%
o8811
8.6%
e8800
8.6%
l7611
 
7.4%
t7523
 
7.3%
i7348
 
7.2%
r6870
 
6.7%
s4666
 
4.6%
d4304
 
4.2%
Other values (13)20545
20.1%
Decimal Number
ValueCountFrequency (%)
152
27.8%
451
27.3%
241
21.9%
327
14.4%
67
 
3.7%
75
 
2.7%
54
 
2.1%
Other Punctuation
ValueCountFrequency (%)
/2164
49.3%
&1124
25.6%
.1105
25.2%
Space Separator
ValueCountFrequency (%)
8919
100.0%
Dash Punctuation
ValueCountFrequency (%)
-371
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin151969
91.6%
Common13870
 
8.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
a14971
 
9.9%
n11004
 
7.2%
o8811
 
5.8%
e8800
 
5.8%
l7611
 
5.0%
t7523
 
5.0%
i7348
 
4.8%
N7044
 
4.6%
r6870
 
4.5%
A6054
 
4.0%
Other values (38)65933
43.4%
Common
ValueCountFrequency (%)
8919
64.3%
/2164
 
15.6%
&1124
 
8.1%
.1105
 
8.0%
-371
 
2.7%
152
 
0.4%
451
 
0.4%
241
 
0.3%
327
 
0.2%
67
 
0.1%
Other values (2)9
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII165839
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a14971
 
9.0%
n11004
 
6.6%
8919
 
5.4%
o8811
 
5.3%
e8800
 
5.3%
l7611
 
4.6%
t7523
 
4.5%
i7348
 
4.4%
N7044
 
4.2%
r6870
 
4.1%
Other values (50)76938
46.4%

CONVERTED_FROM_LEAD_C
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size18.5 KiB
False
11553 
True
7304 
ValueCountFrequency (%)
False11553
61.3%
True7304
38.7%
2021-09-29T09:18:04.100504image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

DNA_STD_DC_LEAD_SOURCE
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION

Distinct53
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size147.4 KiB
Sales
7001 
Channel Marketing
4126 
List Purchase
1854 
Field Marketing
1556 
Trade Show
1523 
Other values (48)
2797 

Length

Max length35
Median length10
Mean length10.22076682
Min length3

Characters and Unicode

Total characters192733
Distinct characters46
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)0.1%

Sample

1st rowChannel Marketing
2nd rowSales
3rd rowSales
4th rowField Marketing
5th rowField Marketing

Common Values

ValueCountFrequency (%)
Sales7001
37.1%
Channel Marketing4126
21.9%
List Purchase1854
 
9.8%
Field Marketing1556
 
8.3%
Trade Show1523
 
8.1%
Web771
 
4.1%
Lead Gen503
 
2.7%
ISR/Partner Outbound314
 
1.7%
Webinar228
 
1.2%
Promotion158
 
0.8%
Other values (43)823
 
4.4%

Length

2021-09-29T09:18:04.302623image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sales7053
24.2%
marketing5691
19.5%
channel4139
14.2%
list1855
 
6.4%
purchase1854
 
6.4%
field1556
 
5.3%
trade1523
 
5.2%
show1523
 
5.2%
web771
 
2.6%
gen503
 
1.7%
Other values (65)2704
 
9.3%

Most occurring characters

ValueCountFrequency (%)
e25175
13.1%
a22198
 
11.5%
n16087
 
8.3%
l13323
 
6.9%
s11004
 
5.7%
r10696
 
5.5%
10315
 
5.4%
i10136
 
5.3%
S9127
 
4.7%
t8792
 
4.6%
Other values (36)55880
29.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter151752
78.7%
Uppercase Letter30281
 
15.7%
Space Separator10315
 
5.4%
Other Punctuation332
 
0.2%
Dash Punctuation23
 
< 0.1%
Open Punctuation15
 
< 0.1%
Close Punctuation15
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e25175
16.6%
a22198
14.6%
n16087
10.6%
l13323
8.8%
s11004
7.3%
r10696
7.0%
i10136
6.7%
t8792
 
5.8%
h7654
 
5.0%
g5958
 
3.9%
Other values (12)20729
13.7%
Uppercase Letter
ValueCountFrequency (%)
S9127
30.1%
M5707
18.8%
C4322
14.3%
P2513
 
8.3%
L2358
 
7.8%
T1680
 
5.5%
F1556
 
5.1%
W1002
 
3.3%
G503
 
1.7%
O451
 
1.5%
Other values (9)1062
 
3.5%
Space Separator
ValueCountFrequency (%)
10315
100.0%
Other Punctuation
ValueCountFrequency (%)
/332
100.0%
Dash Punctuation
ValueCountFrequency (%)
-23
100.0%
Open Punctuation
ValueCountFrequency (%)
(15
100.0%
Close Punctuation
ValueCountFrequency (%)
)15
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin182033
94.4%
Common10700
 
5.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e25175
13.8%
a22198
12.2%
n16087
 
8.8%
l13323
 
7.3%
s11004
 
6.0%
r10696
 
5.9%
i10136
 
5.6%
S9127
 
5.0%
t8792
 
4.8%
h7654
 
4.2%
Other values (31)47841
26.3%
Common
ValueCountFrequency (%)
10315
96.4%
/332
 
3.1%
-23
 
0.2%
(15
 
0.1%
)15
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII192733
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e25175
13.1%
a22198
 
11.5%
n16087
 
8.3%
l13323
 
6.9%
s11004
 
5.7%
r10696
 
5.5%
10315
 
5.4%
i10136
 
5.3%
S9127
 
4.7%
t8792
 
4.6%
Other values (36)55880
29.0%

DNA_STD_DC_LEAD_SOURCE_INBOUND
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size147.4 KiB
Outbound
17963 
Inbound
 
894

Length

Max length8
Median length8
Mean length7.95259055
Min length7

Characters and Unicode

Total characters149962
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOutbound
2nd rowOutbound
3rd rowOutbound
4th rowOutbound
5th rowOutbound

Common Values

ValueCountFrequency (%)
Outbound17963
95.3%
Inbound894
 
4.7%

Length

2021-09-29T09:18:04.471729image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-29T09:18:04.521968image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
outbound17963
95.3%
inbound894
 
4.7%

Most occurring characters

ValueCountFrequency (%)
u36820
24.6%
n19751
13.2%
b18857
12.6%
o18857
12.6%
d18857
12.6%
O17963
12.0%
t17963
12.0%
I894
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter131105
87.4%
Uppercase Letter18857
 
12.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
u36820
28.1%
n19751
15.1%
b18857
14.4%
o18857
14.4%
d18857
14.4%
t17963
13.7%
Uppercase Letter
ValueCountFrequency (%)
O17963
95.3%
I894
 
4.7%

Most occurring scripts

ValueCountFrequency (%)
Latin149962
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
u36820
24.6%
n19751
13.2%
b18857
12.6%
o18857
12.6%
d18857
12.6%
O17963
12.0%
t17963
12.0%
I894
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII149962
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
u36820
24.6%
n19751
13.2%
b18857
12.6%
o18857
12.6%
d18857
12.6%
O17963
12.0%
t17963
12.0%
I894
 
0.6%

DNA_CUSTOM_DC_PRIMARY_COMPETITOR
Categorical

HIGH CORRELATION

Distinct26
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size147.4 KiB
Veeam
3921 
No Competitor
2944 
Cohesity
2101 
CommVault
2078 
Other
1508 
Other values (21)
6305 

Length

Max length46
Median length8
Mean length7.92474943
Min length3

Characters and Unicode

Total characters149437
Distinct characters44
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNone
2nd rowNone
3rd rowDataDomain
4th rowCohesity
5th rowNone

Common Values

ValueCountFrequency (%)
Veeam3921
20.8%
No Competitor2944
15.6%
Cohesity2101
11.1%
CommVault2078
11.0%
Other1508
 
8.0%
NetBackup1208
 
6.4%
Avamar1131
 
6.0%
None1074
 
5.7%
DataDomain938
 
5.0%
TSM366
 
1.9%
Other values (16)1588
8.4%

Length

2021-09-29T09:18:04.698503image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
veeam3921
17.5%
no2944
13.1%
competitor2944
13.1%
cohesity2101
9.4%
commvault2078
9.3%
other1508
 
6.7%
netbackup1208
 
5.4%
avamar1131
 
5.0%
none1074
 
4.8%
datadomain938
 
4.2%
Other values (30)2608
11.6%

Most occurring characters

ValueCountFrequency (%)
e18362
12.3%
o16211
 
10.8%
t15192
 
10.2%
m13143
 
8.8%
a12902
 
8.6%
r7288
 
4.9%
C7125
 
4.8%
i6626
 
4.4%
V6045
 
4.0%
N5778
 
3.9%
Other values (34)40765
27.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter117224
78.4%
Uppercase Letter28468
 
19.1%
Space Separator3598
 
2.4%
Other Punctuation51
 
< 0.1%
Open Punctuation48
 
< 0.1%
Close Punctuation48
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e18362
15.7%
o16211
13.8%
t15192
13.0%
m13143
11.2%
a12902
11.0%
r7288
 
6.2%
i6626
 
5.7%
p4514
 
3.9%
u3768
 
3.2%
h3693
 
3.2%
Other values (12)15525
13.2%
Uppercase Letter
ValueCountFrequency (%)
C7125
25.0%
V6045
21.2%
N5778
20.3%
D2276
 
8.0%
A1610
 
5.7%
O1508
 
5.3%
B1478
 
5.2%
M536
 
1.9%
S495
 
1.7%
P377
 
1.3%
Other values (8)1240
 
4.4%
Space Separator
ValueCountFrequency (%)
3598
100.0%
Open Punctuation
ValueCountFrequency (%)
(48
100.0%
Close Punctuation
ValueCountFrequency (%)
)48
100.0%
Other Punctuation
ValueCountFrequency (%)
\51
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin145692
97.5%
Common3745
 
2.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e18362
12.6%
o16211
11.1%
t15192
10.4%
m13143
 
9.0%
a12902
 
8.9%
r7288
 
5.0%
C7125
 
4.9%
i6626
 
4.5%
V6045
 
4.1%
N5778
 
4.0%
Other values (30)37020
25.4%
Common
ValueCountFrequency (%)
3598
96.1%
\51
 
1.4%
(48
 
1.3%
)48
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII149437
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e18362
12.3%
o16211
 
10.8%
t15192
 
10.2%
m13143
 
8.8%
a12902
 
8.6%
r7288
 
4.9%
C7125
 
4.8%
i6626
 
4.4%
V6045
 
4.0%
N5778
 
3.9%
Other values (34)40765
27.3%

DNA_CUSTOM_DC_INCUMBENT
Categorical

HIGH CORRELATION

Distinct29
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size147.4 KiB
Veeam
3782 
CommVault
2878 
Veritas NetBackup
2532 
Other
2041 
DellEMC Avamar
1954 
Other values (24)
5670 

Length

Max length35
Median length9
Mean length11.58243623
Min length4

Characters and Unicode

Total characters218410
Distinct characters47
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowNone
2nd rowNone
3rd rowDellEMC DataDomain
4th rowIBM Spectrum Protect
5th rowNone

Common Values

ValueCountFrequency (%)
Veeam3782
20.1%
CommVault2878
15.3%
Veritas NetBackup2532
13.4%
Other2041
10.8%
DellEMC Avamar1954
10.4%
DellEMC DataDomain1013
 
5.4%
IBM Spectrum Protect (TSM)1003
 
5.3%
DellEMC Networker800
 
4.2%
Veritas BackupExec800
 
4.2%
Not Sure499
 
2.6%
Other values (19)1555
8.2%

Length

2021-09-29T09:18:04.899299image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
dellemc3793
12.5%
veeam3782
12.5%
veritas3332
11.0%
commvault2878
9.5%
netbackup2532
 
8.4%
other2041
 
6.7%
avamar1954
 
6.4%
ibm1052
 
3.5%
spectrum1052
 
3.5%
protect1052
 
3.5%
Other values (34)6855
22.6%

Most occurring characters

ValueCountFrequency (%)
e26330
 
12.1%
a20652
 
9.5%
t17094
 
7.8%
m13626
 
6.2%
r13070
 
6.0%
11466
 
5.2%
l10677
 
4.9%
V9992
 
4.6%
u8167
 
3.7%
o7326
 
3.4%
Other values (37)80010
36.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter151113
69.2%
Uppercase Letter53511
 
24.5%
Space Separator11466
 
5.2%
Open Punctuation1121
 
0.5%
Close Punctuation1121
 
0.5%
Other Punctuation78
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e26330
17.4%
a20652
13.7%
t17094
11.3%
m13626
9.0%
r13070
8.6%
l10677
7.1%
u8167
 
5.4%
o7326
 
4.8%
c7053
 
4.7%
i5151
 
3.4%
Other values (14)21967
14.5%
Uppercase Letter
ValueCountFrequency (%)
V9992
18.7%
C6717
12.6%
M6107
11.4%
D6059
11.3%
E4744
8.9%
B4462
8.3%
N4258
8.0%
S2584
 
4.8%
A2337
 
4.4%
O2041
 
3.8%
Other values (9)4210
7.9%
Space Separator
ValueCountFrequency (%)
11466
100.0%
Open Punctuation
ValueCountFrequency (%)
(1121
100.0%
Close Punctuation
ValueCountFrequency (%)
)1121
100.0%
Other Punctuation
ValueCountFrequency (%)
/78
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin204624
93.7%
Common13786
 
6.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
e26330
 
12.9%
a20652
 
10.1%
t17094
 
8.4%
m13626
 
6.7%
r13070
 
6.4%
l10677
 
5.2%
V9992
 
4.9%
u8167
 
4.0%
o7326
 
3.6%
c7053
 
3.4%
Other values (33)70637
34.5%
Common
ValueCountFrequency (%)
11466
83.2%
(1121
 
8.1%
)1121
 
8.1%
/78
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII218410
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e26330
 
12.1%
a20652
 
9.5%
t17094
 
7.8%
m13626
 
6.2%
r13070
 
6.0%
11466
 
5.2%
l10677
 
4.9%
V9992
 
4.6%
u8167
 
3.7%
o7326
 
3.4%
Other values (37)80010
36.6%

DNA_CUSTOM_DC_SEGMENT
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size147.4 KiB
Commercial
11743 
Unclassified
4430 
Enterprise
2581 
CAE
 
103

Length

Max length12
Median length10
Mean length10.43161691
Min length3

Characters and Unicode

Total characters196709
Distinct characters18
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUnclassified
2nd rowUnclassified
3rd rowUnclassified
4th rowCommercial
5th rowUnclassified

Common Values

ValueCountFrequency (%)
Commercial11743
62.3%
Unclassified4430
 
23.5%
Enterprise2581
 
13.7%
CAE103
 
0.5%

Length

2021-09-29T09:18:05.072656image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-29T09:18:05.125172image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
commercial11743
62.3%
unclassified4430
 
23.5%
enterprise2581
 
13.7%
cae103
 
0.5%

Most occurring characters

ValueCountFrequency (%)
m23486
11.9%
i23184
11.8%
e21335
10.8%
r16905
8.6%
c16173
8.2%
l16173
8.2%
a16173
8.2%
C11846
6.0%
o11743
6.0%
s11441
5.8%
Other values (8)28250
14.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter177646
90.3%
Uppercase Letter19063
 
9.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
m23486
13.2%
i23184
13.1%
e21335
12.0%
r16905
9.5%
c16173
9.1%
l16173
9.1%
a16173
9.1%
o11743
6.6%
s11441
6.4%
n7011
 
3.9%
Other values (4)14022
7.9%
Uppercase Letter
ValueCountFrequency (%)
C11846
62.1%
U4430
 
23.2%
E2684
 
14.1%
A103
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Latin196709
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
m23486
11.9%
i23184
11.8%
e21335
10.8%
r16905
8.6%
c16173
8.2%
l16173
8.2%
a16173
8.2%
C11846
6.0%
o11743
6.0%
s11441
5.8%
Other values (8)28250
14.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII196709
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
m23486
11.9%
i23184
11.8%
e21335
10.8%
r16905
8.6%
c16173
8.2%
l16173
8.2%
a16173
8.2%
C11846
6.0%
o11743
6.0%
s11441
5.8%
Other values (8)28250
14.4%

OPPORTUNITY_SUB_TYPE_C
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size147.4 KiB
Regular
17783 
MSP
 
1012
Renewal
 
25
NFR
 
21
POC
 
16

Length

Max length7
Median length7
Mean length6.777483163
Min length3

Characters and Unicode

Total characters127803
Distinct characters16
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRegular
2nd rowRegular
3rd rowRegular
4th rowRegular
5th rowRegular

Common Values

ValueCountFrequency (%)
Regular17783
94.3%
MSP1012
 
5.4%
Renewal25
 
0.1%
NFR21
 
0.1%
POC16
 
0.1%

Length

2021-09-29T09:18:05.292804image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-29T09:18:05.351378image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
regular17783
94.3%
msp1012
 
5.4%
renewal25
 
0.1%
nfr21
 
0.1%
poc16
 
0.1%

Most occurring characters

ValueCountFrequency (%)
e17833
14.0%
R17829
14.0%
l17808
13.9%
a17808
13.9%
g17783
13.9%
u17783
13.9%
r17783
13.9%
P1028
 
0.8%
M1012
 
0.8%
S1012
 
0.8%
Other values (6)124
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter106848
83.6%
Uppercase Letter20955
 
16.4%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R17829
85.1%
P1028
 
4.9%
M1012
 
4.8%
S1012
 
4.8%
N21
 
0.1%
F21
 
0.1%
O16
 
0.1%
C16
 
0.1%
Lowercase Letter
ValueCountFrequency (%)
e17833
16.7%
l17808
16.7%
a17808
16.7%
g17783
16.6%
u17783
16.6%
r17783
16.6%
n25
 
< 0.1%
w25
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin127803
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e17833
14.0%
R17829
14.0%
l17808
13.9%
a17808
13.9%
g17783
13.9%
u17783
13.9%
r17783
13.9%
P1028
 
0.8%
M1012
 
0.8%
S1012
 
0.8%
Other values (6)124
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII127803
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e17833
14.0%
R17829
14.0%
l17808
13.9%
a17808
13.9%
g17783
13.9%
u17783
13.9%
r17783
13.9%
P1028
 
0.8%
M1012
 
0.8%
S1012
 
0.8%
Other values (6)124
 
0.1%

Interactions

2021-09-29T09:17:10.154768image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-29T09:17:10.260627image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-29T09:17:10.349078image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-29T09:17:10.436019image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-29T09:17:10.523349image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-29T09:17:10.610980image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-29T09:17:10.692838image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-29T09:17:10.778574image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-29T09:17:10.866044image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-29T09:17:10.949260image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-29T09:17:11.033008image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-29T09:17:11.119749image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-29T09:17:11.200024image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-29T09:17:11.281558image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-29T09:17:11.363932image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-29T09:17:11.448263image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-29T09:17:11.535063image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-29T09:17:11.619683image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-29T09:17:11.703144image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-29T09:17:11.784482image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-29T09:17:11.867610image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-29T09:17:11.950821image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-29T09:17:12.034382image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-29T09:17:12.209297image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-29T09:17:12.289351image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-29T09:17:12.367496image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-29T09:17:12.448554image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-29T09:17:12.529905image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-29T09:17:12.612424image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-29T09:17:12.691785image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-29T09:17:12.773371image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-29T09:17:12.856639image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-29T09:17:12.936162image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-29T09:17:13.014305image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-29T09:17:13.098960image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-29T09:17:13.175463image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-29T09:17:13.254156image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-29T09:17:13.333224image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-29T09:17:13.415163image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-29T09:17:13.502121image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-29T09:17:13.580103image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-29T09:17:13.660483image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-29T09:17:13.738962image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-29T09:17:13.818706image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-29T09:17:13.898301image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-29T09:17:13.979265image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-29T09:17:14.064356image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-29T09:17:14.148128image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-29T09:17:14.230016image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-29T09:17:14.313808image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-29T09:17:14.400364image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-29T09:17:14.487944image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-29T09:17:14.660471image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-29T09:17:14.745527image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-29T09:17:14.834550image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-29T09:17:14.918806image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-29T09:17:15.002735image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-29T09:17:15.092596image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-29T09:17:15.173998image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-29T09:17:15.257614image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-29T09:17:15.341403image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-29T09:17:15.428352image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-29T09:17:15.516418image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-29T09:17:15.598465image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-29T09:17:15.682169image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-29T09:17:15.763999image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-29T09:17:15.847498image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-29T09:17:15.930145image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-29T09:17:16.012259image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-29T09:17:16.099485image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-29T09:17:16.184961image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-29T09:17:16.268157image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-29T09:17:16.354544image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-29T09:17:16.441943image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-29T09:17:16.532481image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-29T09:17:16.617597image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2021-09-29T09:17:52.053176image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-29T09:17:52.134428image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-29T09:17:52.217331image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-29T09:17:52.298930image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-29T09:17:52.381339image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-29T09:17:52.463632image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-29T09:17:52.547742image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-29T09:17:52.635813image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-29T09:17:52.717552image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-29T09:17:52.796509image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-29T09:17:52.879454image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-29T09:17:52.969519image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-29T09:17:53.055890image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-29T09:17:53.141319image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-29T09:17:53.228539image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-29T09:17:53.314684image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-29T09:17:53.400245image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-29T09:17:53.482244image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-29T09:17:53.569945image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-29T09:17:53.651893image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-29T09:17:53.735852image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-29T09:17:53.818819image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-29T09:17:53.904999image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-29T09:17:53.992027image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-29T09:17:54.073355image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-29T09:17:54.157267image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-29T09:17:54.236907image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-29T09:17:54.318265image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-29T09:17:54.408115image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-29T09:17:54.490320image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-29T09:17:54.577936image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-29T09:17:54.665825image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-29T09:17:54.751384image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-29T09:17:54.841176image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-29T09:17:54.939468image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-29T09:17:55.035403image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-29T09:17:55.126041image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-29T09:17:55.217267image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-29T09:17:55.310362image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-29T09:17:55.399620image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-29T09:17:55.487806image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-29T09:17:55.580874image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-29T09:17:55.667544image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-29T09:17:55.755183image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-29T09:17:55.843261image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-29T09:17:55.934830image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-29T09:17:56.027056image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-29T09:17:56.114725image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-29T09:17:56.202927image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-29T09:17:56.289547image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-29T09:17:56.378679image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-29T09:17:56.466761image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-29T09:17:56.555381image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2021-09-29T09:18:05.464257image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-09-29T09:18:05.704020image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-09-29T09:18:05.941890image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-09-29T09:18:06.192194image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-09-29T09:18:06.414059image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-09-29T09:17:56.845375image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2021-09-29T09:17:57.472516image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

df_indexOPPORTUNITY_IDACCOUNT_IDDNA_STD_DC_END_RESULTADVANCED_PAST_STAGE_1_CADVANCED_PAST_STAGE_2_CDNA_STD_DC_AMOUNTDNA_CUSTOM_DC_ISR_LEDDNA_CUSTOM_DC_SDR_LEDDNA_STD_AC_NUMBER_OF_EMPLOYEESDNA_CUSTOM_DC_PREV_OPPS_COUNTDNA_CUSTOM_DC_PREV_OPPS_LOST_COUNTDNA_STD_AC_ANNUAL_REVENUEDAYS_IN_BUSINESS_JUSTIFICATION_CDAYS_IN_CONSENSUS_CDAYS_IN_DISCOVERY_CDAYS_IN_NEGOTIATE_CLOSE_CDAYS_IN_TECHNICAL_VALIDATION_CDAYS_IN_PO_WITH_CHANNEL_CDNA_CUSTOM_DC_DURATION_POCDNA_STD_DC_MKTG_NURTURE_TIMEDNA_CUSTOM_DC_CONTACTS_ACTIVEDNA_STD_DC_EVENTS_IA_AFTER_OPPTY_COUNTDNA_STD_DC_EVENTS_IA_BEFORE_OPPTY_COUNTDNA_STD_DC_EVENTS_TOTAL_IA_COUNTDNA_STD_DC_MKTG_IA_AFTER_OPPTY_COUNTDNA_STD_DC_MKTG_IA_BEFORE_OPPTY_COUNTDNA_STD_DC_MKTG_TOTAL_IA_COUNTDNA_STD_DC_TASKS_IA_AFTER_OPPTY_COUNTDNA_ML_OPPORTUNITY_LIFE_DAYSSUB_REGION_COWNER_SUB_REGION_CCONVERTED_FROM_LEAD_CDNA_STD_DC_LEAD_SOURCEDNA_STD_DC_LEAD_SOURCE_INBOUNDDNA_CUSTOM_DC_PRIMARY_COMPETITORDNA_CUSTOM_DC_INCUMBENTDNA_CUSTOM_DC_SEGMENTOPPORTUNITY_SUB_TYPE_C
050064000000g9NB0AAM0014000001bs9dUAAQFalseFalseFalse150000.00FalseFalse570503.00.00.05.252100e+040.000.000.000.000.00.00.000.00.00.00.00.00.00.00.0283EastEastFalseChannel MarketingOutboundNoneNoneUnclassifiedRegular
180064000000g9XLjAAM0014000001bsF5mAAEFalseFalseFalse150000.00FalseFalse26300.00.00.02.359056e+100.000.000.000.000.00.00.00181.00.00.00.00.00.00.00.0286UnknownUnknownFalseSalesOutboundNoneNoneUnclassifiedRegular
290066f000012ZxZYAA00014000001bsF5mAAEFalseTrueFalse2688777.00FalseFalse26300.03.03.02.359056e+100.0036.33143.710.000.00.00.01765175.02.00.010.06.09.019.062.0179S. TOLAS. TOLATrueSalesOutboundDataDomainDellEMC DataDomainUnclassifiedRegular
3140061W000014Qz7yQAC0014000001bsF6xAAEFalseTrueTrue272605.76FalseFalse4925.01.01.01.311677e+07269.790.000.0060.040.06.033.0115716.00.00.00.00.00.00.029.0339Ohio Valley OHVAOhio Valley OHVAFalseField MarketingOutboundCohesityIBM Spectrum ProtectCommercialRegular
4180064000000g9XMwAAM0014000001bsF6xAAEFalseTrueFalse250000.00FalseTrue4925.00.00.01.311677e+070.000.000.000.000.00.00.0017.00.00.00.00.00.00.00.0359UnknownUnknownFalseField MarketingOutboundNoneNoneUnclassifiedRegular
5200064000000g9bCyAAI0014000001d1blnAAAFalseFalseFalse300000.00FalseFalse2.00.00.09.090000e+040.000.000.000.000.00.00.001.00.00.00.00.00.00.00.0282Bay AreaBay AreaFalseSalesOutboundNoneNoneCommercialRegular
6210061W00000svAX7QAM0014000001d1btTAAQFalseFalseFalse250000.00FalseFalse260000.04.01.08.257400e+100.000.00643.290.000.00.00.081367.00.00.03.00.00.069.097.0642WestCarolinas/TNFalseSalesOutboundDataDomainVeritas NetBackupEnterpriseRegular
7220061W00000svFRdQAM0014000001d1btTAAQFalseTrueFalse250000.00FalseFalse260000.04.01.08.257400e+100.000.00643.290.000.00.00.081367.00.00.03.00.00.069.097.0642WestCarolinas/TNFalseSalesOutboundNetBackupDellEMC DataDomainEnterpriseRegular
8230061W0000120beFQAQ0014000001d1btTAAQFalseTrueTrue75000.00FalseFalse260000.03.01.08.257400e+100.000.000.000.000.00.00.070767.00.00.03.00.00.069.0152.0606Datos - AmericasDatos - AmericasFalseSalesOutboundNoneVeritas NetBackupUnclassifiedRenewal
9240061W000013jBITQA20014000001d1btTAAQFalseFalseFalse220000.00FalseFalse260000.06.01.08.257400e+100.000.00230.000.000.00.00.0113664.00.00.03.00.00.069.063.0229Great PlainsGreat PlainsTruePPCOutboundNetBackupVeritas NetBackupCommercialRegular

Last rows

df_indexOPPORTUNITY_IDACCOUNT_IDDNA_STD_DC_END_RESULTADVANCED_PAST_STAGE_1_CADVANCED_PAST_STAGE_2_CDNA_STD_DC_AMOUNTDNA_CUSTOM_DC_ISR_LEDDNA_CUSTOM_DC_SDR_LEDDNA_STD_AC_NUMBER_OF_EMPLOYEESDNA_CUSTOM_DC_PREV_OPPS_COUNTDNA_CUSTOM_DC_PREV_OPPS_LOST_COUNTDNA_STD_AC_ANNUAL_REVENUEDAYS_IN_BUSINESS_JUSTIFICATION_CDAYS_IN_CONSENSUS_CDAYS_IN_DISCOVERY_CDAYS_IN_NEGOTIATE_CLOSE_CDAYS_IN_TECHNICAL_VALIDATION_CDAYS_IN_PO_WITH_CHANNEL_CDNA_CUSTOM_DC_DURATION_POCDNA_STD_DC_MKTG_NURTURE_TIMEDNA_CUSTOM_DC_CONTACTS_ACTIVEDNA_STD_DC_EVENTS_IA_AFTER_OPPTY_COUNTDNA_STD_DC_EVENTS_IA_BEFORE_OPPTY_COUNTDNA_STD_DC_EVENTS_TOTAL_IA_COUNTDNA_STD_DC_MKTG_IA_AFTER_OPPTY_COUNTDNA_STD_DC_MKTG_IA_BEFORE_OPPTY_COUNTDNA_STD_DC_MKTG_TOTAL_IA_COUNTDNA_STD_DC_TASKS_IA_AFTER_OPPTY_COUNTDNA_ML_OPPORTUNITY_LIFE_DAYSSUB_REGION_COWNER_SUB_REGION_CCONVERTED_FROM_LEAD_CDNA_STD_DC_LEAD_SOURCEDNA_STD_DC_LEAD_SOURCE_INBOUNDDNA_CUSTOM_DC_PRIMARY_COMPETITORDNA_CUSTOM_DC_INCUMBENTDNA_CUSTOM_DC_SEGMENTOPPORTUNITY_SUB_TYPE_C
18847563040061W000014u2mFQAQ0014000001brDkpAAEFalseFalseFalse450000.0FalseTrue130.01.01.02.461044e+070.00.0344.040.00.00.00.011903.00.00.00.00.00.00.05.0343Great PlainsGreat PlainsTrueTrade ShowOutboundCommVaultCommVaultCommercialRegular
18848563070064000000fiFbDAAU0014000001brDkpAAEFalseFalseTrue400000.0FalseFalse130.00.00.02.461044e+070.00.00.000.00.00.00.005.00.00.00.00.00.00.00.0217UnknownUnknownFalseSalesOutboundNoneNoneUnclassifiedRegular
18849563080064000000fiN8sAAE0014000001brGFbAAMFalseFalseFalse200000.0FalseTrue8000.00.00.01.061744e+090.00.00.000.00.00.00.002.00.00.00.00.00.00.00.0228EastEastFalseField MarketingOutboundNoneNoneUnclassifiedRegular
18850563090064000000ge6InAAI0014000001brGN6AAMFalseTrueFalse140000.0FalseFalse450.00.00.08.595864e+070.00.00.000.00.00.00.08014.00.00.00.00.00.06.011.0297UnknownUnknownFalseSalesOutboundNoneNoneUnclassifiedRegular
18851563160064000000g7kXCAAY0014000001brKKxAAMFalseFalseFalse100000.0FalseFalse3.00.00.03.014360e+050.00.00.000.00.00.00.000.00.00.00.00.00.00.00.094NYNJ/MidAtlanticNYNJ/MidAtlanticFalseTrade ShowOutboundNoneNoneUnclassifiedRegular
18852563170064000000g8t7nAAA0014000001brlgvAAAFalseFalseFalse80000.0FalseFalse2.00.00.05.787300e+040.00.00.000.00.00.00.000.00.00.00.00.00.00.00.0287EastEastFalseSalesOutboundNoneNoneUnclassifiedRegular
18853563180064000000g80QTAAY0014000001brPVjAAMFalseTrueFalse280000.0FalseFalse140.00.00.02.909489e+070.00.00.000.00.00.00.000.00.00.00.00.00.00.00.0176EastEastFalseChannel MarketingOutboundNoneNoneUnclassifiedRegular
18854563190064000000g93lIAAQ0014000001brsMMAAYFalseFalseFalse70000.0FalseFalse11.00.00.02.082534e+060.00.00.000.00.00.00.000.00.00.00.00.00.00.00.0222EastEastFalseChannel MarketingOutboundNoneNoneUnclassifiedRegular
18855563200064000000g9AQ7AAM0014000001brwmMAAQFalseFalseFalse160000.0FalseFalse5802.00.00.03.894936e+070.00.00.000.00.00.00.000.00.00.00.00.00.00.00.0222EastEastFalseChannel MarketingOutboundNoneNoneUnclassifiedRegular
18856563210064000000g8HnBAAU0014000001brYdYAAUFalseFalseFalse75000.0FalseTrue6.00.00.01.201174e+060.00.00.000.00.00.00.001.00.00.00.00.00.00.00.067Bay AreaBay AreaFalseWebInboundNoneNoneCommercialRegular